Body: Your name, e-mail, phone number, organization, postal mail, Database
you require, purpose for which you will use the database, time and date at
which you sent the fax with the signed license agreement. Along with the
email send a pdf file with the signed license agreement.

Send by postal mail the original of your signed license agreement to the
following address:

Miguel Ángel Ferrer Ballester

Departamento de Señales y Comunicaciones

Universidad de Las Palmas de Gran Canaria

Campus de Tafira s/n

35017 Las Palmas de Gran Canaria, SPAIN

Once the fax and email of the license agreement have been received, you
will receive an email with instructions to download the database. After
you finish the download, please notify by email that you have successfully
completed the transaction.

A Hidden Markov
Model (HMM) Toolbox within the Matlab environment is available. In this
toolbox, the conventional techniques for the continuous and discrete HMM
are developed for the training as well as for the test phases. The
ability to make different groups of components for the vector pattern is
provided. Multi-labeling techniques for the discrete HMM is also
provided. The toolbox includes procedures suitable for the classical
applications based on the HMM, as pattern recognition, speech
recognition and DNA sequence analysis.

This toolbox is
distributed as binary (dll files) and source code format. For a wide
promotion, we ask the users to make a reference to the paper:

The database consists of 10 different acquisitions of 150 people by a desk
scanner. The 1500 images have been taken from the users’ right hand. The
user in our system can place the hand palm freely over the scanning
surface; pegs, templates or any other annoying method for the users to
capture their hands are not used. The hand contour with landmarks (valleys
and tips of the fingers) and the segmented palms are also provided. They
have been obtained automatically without supervision as described in:

The signatures are in "jpg" format, 256 gray levels and 120 dpi of
resolution. The files of the hands are named xxx\manoxxx_yy.jpg where xxx
is the number of the signer and yy its repetition. The palm images are
given in the files named xxx\palmaxxx_yy.jpg. The hand contour and
landmarks are given in the Matlab2007 file metadata.mat. How to use these
files can be seen in the file ReadDatabase.m.

Our hands database consists of 10 times 3 acquisitions (visible, 850nm and
1470nm bands) from 100 people. The 3000 images were taken from the users’
right hand. Most of the users are between 23 to 40 years old.
Approximately half of the database volunteers are male. The user in our
system can place the hand palm freely over the plate; pegs, templates or
any other annoying method for the users to capture their hands are not
used. The cameras acquire the hand dorsum image. The image in the 1470nm
band is acquired by a XENICS camera XEVA 1.7-320 with an InGaAs sensor,
sensitive from 900 to 1700nm, with a band pass filter lens centered on
1470nm and bandwidth of 250nm. The image in the visible band is acquired
with a color webcam quickcam E2500, with a resolution of 640x480 pixels.
The image in the NIR band is acquired with a color webcam quickcam E2500,
with a resolution of 640x480 pixels and a high pass filter lens with
cutoff wavelength at 850nm. The procedure is described in:

The signatures are in "bmp" format as given by the cameras. The files of
the hands are named yyy\xxx_yyy_rr.bmp where xxx is the band (xxx= ‘vis’,
850 or 1450), yyy is the user number and rr its repetition.

Our contactless hands database consists of 10 times 2 acquisitions
(visible and 850nm) from 100 people. The 2000 images were taken from the
users’ right hand. Most of the users are between 23 to 40 years old.
Approximately half of the database volunteers are male. The user places
his or her hand over the camera and touchless adjusts the position and
pose of the hand in order to overlap with the hand mask drawn on the
device screen. When the hand and mask overlap more than 70%, the device
automatically acquires both the IR and visible image. Detail can be seen
in:

The acquisition device used consists of two inexpensive, standard web cams
that obtain images of the hand at the same time. The so called infrared (IR)
webcam acquires images in the infrared band (750 to 1000nm) and the so
called visible (V) camera acquires images in the visible range (400 to
700nm). The IR webcam was created by simply taking out the webcam lens
that eliminates the infrared radiation and adding a filter that eliminates
the visible band. We used Kodak filter No 87 FS4-518 and No 87c FS4-519
with no transmittance below 750 nm.

The infrared illumination is composed of a set of 24 GaAs
infrared emitting diode (CQY 99) with a peak wavelength emis-sion of 925
nm and a spectral bandwidth of 40 nm. The diodes were placed in an
inverted U shape with the IR and V webcams in the middle. The open part of
the U shape will coincide with the wrists of the hand image. The focus of
the IR webcam lens is adjusted manually the first time the webcam is used.

The signatures are in "bmp" format as given by the webcam. The files of
the hands in visible band and infrared band are named xxx\visible_xxx_yy.bmp
and xxx\Infraro_xxx_yy.bmp where xxx is the number of the signer and yy
its repetition. The segmented palm images are given in the files named
xxx\palma_xxx_yy.jpg and the contour of the visible image is given in the
files xxx\Icontvisible_xxx_yy. How to use these files can be seen in the
file ReadDatabase.m.

The GPDShandsSWIRhyperspectral database consists of 10 different samples
from154 people. Each sample is composed of 350 images acquired by the
hyperspectral device, so the total number of images on the database is
images
in 256 bands between 900nm and 1600nm. The users were allowed to wear
wristwatches or wristlets. The age of the users ranged from 18 to 60
years, 86% of them between 18 and 30. Approximately 70% are students and
teachers from our university and the remaining 30% are administration and
cleaning staff. In the database, 64% of the users are male. For the first
24 users, we acquired the hyperspectral images from each hand side, palm
and dorsum.

The acquisition device used consist on a Xenics® Xeva-1.7-320 camera which
is based on an InGaAs detector, sensitive from 900 to 1700nm. The camera
provides 256 gray level images with a resolution of 320 by 256 pixels. We
used this in conjunction with a SPECIM® Imspector N17E optical
spectrograph with numerical aperture f/2.0. This transforms the SWIR
camera into a line spectral imaging device, as seen in Figure 2 which
shows the reflectance along a longitudinal line (x axis) that crosses four
fingers. The spectrographic images consist of 320 pixels in the x axis and
256 bands in the wavelength axis. As the aperture is f/2.0 and the
distance from the lens to the plate is 41 cm, the x dimension covered is
21 cm. Therefore the horizontal resolution is approximately 38 dpi.

To add the information for the y axis, a rotating mirror scanner is
attached to the objective lens of the spectrograph. As the mirror scanner
rotates through 40º, the y spatial dimension covered is 29 cm.

The acquisition procedure is as follows. The user is asked to place the
hand dorsum over the plate with the hand palm up; we therefore acquire the
hyperspectral image of the hand palm. Once the hand is still, the mirror
starts to rotate from minus 20 to plus 20 degrees. The camera acquires
around 12 pictures per second. As the procedure takes 30 seconds
approximately, we have 350 images in the y axis, with a vertical
resolution of 30 dpi approximately. A reconstruction stage makes the
hyperspectral cube a coherent object.

The XEVA 1.7-320 had a pixel operability of 99%. It produced 298 erroneous
pixels the values of which we interpolated as the mean of the content of
the pixels immediately above and below. We did not use the average of all
the surrounding pixels because of row contiguous pixel errors.

The illumination system consists of two tungsten filament bulbs with a
radiation spectrum from 400 to 1600nm. Most of the bulb energy is centered
in the NIR. As can be seen in Figure 2, the bulbs are situated at
approximately the same distance from each side of the hand, to avoid
shadows or unbalanced lighting.

The database consists of 10 different acquisitions of 102 people. The
samples were acquired in two separated session one week: five the first
time and
other five samples the second session. The 1020 images have been taken
from the users’ right hand. The system to capture near infrared images of
the hand dorsum consists of two arrays of 64 LEDs in the band of 850nm, a
CCD gigabit Ethernet PULNIX TM3275 camera with a high pass IR filter with
750nm as cutoff frequency, and a handle with two pegs for positional
reference as described in:

The signatures are in "bmp" format as given by the camera. The files of
the hand veins are named xxx\mano-xxx-yyy.bmp where xxx is the number of
the signer and yyy its repetition. A readdatabase.m file is provided.

GPDS100VeinsCMOScylindrical database

The database consists of 10 different acquisitions of 103 people. The
samples were acquired in two separated session one week: five the first
time and other five samples the second session. The 1030 images have been
taken from the users’ right hand. The system to capture near infrared
images of the hand dorsum consists of two arrays of 64 LEDs in the band of
850nm and a CMOS webcam with a high pass IR filter with 750nm as cutoff
frequency, and a cylindrical handle with two pegs for positional
reference.

The users of VeinsCMOScylindrical and CMOSergonimic database are the same.

The signatures are in "bmp" format as given by the camera. The files of
the hand veins are named xxx\venas_xxx_yy.bmp where xxx is the number of
the signer and yy its repetition. A readdatabase.m file is provided.

GPDS100VeinsCMOSergonomic database

The database consists of 10 different acquisitions of 103 people. The
samples were acquired in two separated session one week: five the first
time and other five samples the second session. The 1030 images have been
taken from the users’ right hand. The system to capture near infrared
images of the hand dorsum consists of two arrays of 64 LEDs in the band of
850nm and a CMOS webcam with a high pass IR filter with 750nm as cutoff
frequency, and an ergonomic handle which fix the hand position in a
suitable way for the user.

The users of VeinsCMOScylindrical and CMOSergonimic database are the same.

The signatures are in "bmp" format as given by the camera. The files of
the hand veins are named xxx\venas_xxx_yy.bmp where xxx is the number of
the signer and yy its repetition. A readdatabase.m file is provided.

The pictures are in "jpg" format. The files are named xxx\labio-xx-yy.jpg
where xx is the number of the signer and yy its repetition.

A file in Matlab to read the database is provided: ReadDatabase.m

gpds SIGNATURE

CORPORA

GPDSsyntheticSignature database

Off line signature database. It contains data from 4000 synthetic
individuals: 24 genuine signatures for each individual, plus 30 forgeries
of his/her signature. All the signatures were generated with different
modeled pens. This database replaces previous signatures databases.

The synthetic users have been generated following the procedure described
at:

The signatures are in "jpg" format and equivalent resolution of 600 dpi.
The files of the genuine signatures are named xxx\c-xxx-yy.png and the
files of the forgeries are named xxx\cf-xxx-yy.png where xxx is the number
of the signer and yy its repetition.

For performance reference, our results with previous public databases
using the verifier used at:

Off line signature
database. It contains data from 960 individuals: 24 genuine signatures for
each individual, plus 30 forgeries of his/her signature. The 24 genuine
specimens of each signer were collected in a single day writing sessions.
The forgeries were produced from the static image of the genuine
signature. Each forger was allowed to practice the signature for as long
as s/he wishes. Each forger imitated 3 signatures of 5 signers in a single
day writing session. The genuine signatures shown to each forger are
chosen randomly from the 24 genuine ones. Therefore for each genuine
signature there are 30 skilled forgeries made by 10 forgers from 10
different genuine specimens.

The signatures are in
"bmp" format, in black and white and 300 dpi. The files of the genuine
signatures are named xxx\c-xxx-yy.bmp and the files of the forgeries are
named xxx\cf-xxx-yy.bmp where xxx is the number of the signer and yy its
repetition

As the background of
the scanned signatures is well contrasted with the darker signature
strokes, the signature images where binarized by thresholding with a fixed
threshold and a sort of hair sticking out from signature strokes was
eliminated [1]

The off line signature
verification competition held during the 12th International Conference on
Frontiers in Handwriting Recognition (ICFHR 2010, Kolkata, India) used as
database a subset of the GPDS960Signature database.

As training subset,
the 4NSigCom2010 used 4 genuine signatures of the individuals 301 to 700
of the GPDS960signature corpus. The files of the genuine signatures are
named Trainingset\xxx\c-xxx-yy.bmp being xxx the id of the signer which
goes from 301 to 700 and yy the repetition from 01 to 04

A Matlab scrip to read
and display the train images is provided: ReadTrainingSignatures.m

For testing, 30000
questioned signature images obtained from the GPDS960signature database
were used. The test data includes original signatures of GPDS960signature
signers 301 to 700, simulated forgeries of each user and random signatures
from users 701 to 960. The test files has been named c-xxxxx-yyy.bmp being
xxxxx the number of file from 00001 to 30000 and yyy the id of the signer
identity claimed from 301 to 700.

A Matlab scrip to read
and display the trest images is provided: ReadTestSigantures.m

To evaluate your
automatic signature verifier with the 4NSigComp2010 Scenario 2 database, a
matlab program called EvaluateASV is provided. As it has been done, this
program needs the program asv.m and the file
“4nSigCompSignatureIdentification.mat” which contains the matrix called
sign.

The program asv.m
should be a matlab function defined as:

Function decision=asv(signature,id)

Where signature is the
image of the signature to be verified and id is the identity claimed.
Decision is supposed 1 if the signature is accepted as genuine and 0 if
the signature is considered a forgery.

The mean of the sign
matrix values are the next: The signature c-xxxx-yyy.bmp of the test set
corresponds to the repetition sign(xxxx,2) of the signer sign(xxxx,1) in
the GPDS 960Signature database; The yyy identity claimed by signature c-xxxx-yyy.bmp
is equal to sign(xxxx,3). Finally, if sign(xxxx,4) is equal to 0 means
that c-xxxx-yy.bmp is a genuine signature; if sign(xxxx,4) is equal to 1
means that c-xxxx-yyy.bmp is a imitation or simulated forgery while if
sign(xxxx,4) is equal to 2 means that c-xxxx-yyy.bmp corresponds to random
forgery.

This database contains a gray level version of genuine signatures and
imitations of the GPDS960Signature database.

Due to a move, unfortunately we lost the signatures of 79 users and 143
imitations of the remainder signers. So, the GPDS960GRAYsignature database
consists of 881 users, 21144 genuine signatures and 26317 imitations.
Total: 47485 signatures.

The lost users and imitations are specified in the ReadDatabase.m
file.

In this case, the signatures are in "png" format and have been scanned at
600 dpi. The files of the genuine signatures are named xxx\c-xxx-yy.png
and the files of the forgeries are named xxx\cf-xxx-yy.png where xxx is
the number of the signer and yy its repetition.

This version of the data base has been obtained scanning the sheets again
at 600dpi. So, the segmentation errors are supposed different to those of
GPDS960Signature database

The submitted paper blended the MCYT (http://atvs.ii.uam.es/databases.jsp) and GPDS960GRAYSignatures database with the check
database to obtain a synthetic signature database with distorted gray
levels. We used the multiply blend mode which multiplies the check image
by the signature one. As we overlay gray level strokes, each stroke
results in a new darker gray level.

The next Matlab files are provided:

1. Read
the check database divided in three gray level distortions as in the
submitted paper: ReadCheckDatabase.m.

2.Blend a
signature with a given signature: BlendSignaturewithCheck.m

3.As we
have used texture parameters based on Local Binary Patterns (LB), Local
Directional Pattern (LDP) and Local Derivative Pattern (LDerivP), the
programs to work them out are also provided in the next Matlab files:
LBP.m, LDP.m and LDeriv.m